Embodied AI Tasks.
In recent times, we have seen a proliferation of Embodied AI tasks. Benchmarks on indoor navigation use point-goal specification [1]}, [2]}, object-goal [3]}, [4]}, room navigation [5]}, and language-guided navigation [6]}, [7]}. Some interactive tasks study the agent's ability to follow natural language instruction such as ALFRED [8]} and TEACh [9]} while others focus on rearranging objects following a geometric goal or predicate based specification [10]}, [11]}, [12]}, [13]}. [14]} provides a summary of rearrangement tasks. All these tasks require an explicit goal specification lifting the burden of learning semantic compatibility of objects and their locations in the house from the agent. In contrast, in this work, we argue that agents shouldn't require an explicit goal specification to perform household tasks such as tidying up the house. Instead, it should use its common sense knowledge to infer the human-preferred goal state.
